StockAnalysisSystem/models.py at main · chm020924/StockAnalysisSystem · GitHub

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import pandas as pd
import numpy as np

def calculate_enhanced_fundamental_score(stock_code, df, alpha_factors):
    """增强版基本面评分"""
    try:
        score_details = {}
        total_score = 0

        # 1. 估值合理性 (25分)
        pe_score = 0
        pb_score = 0

        if '市盈率' in df.columns and pd.notna(df['市盈率'].iloc[-1]):
            pe_ratio = df['市盈率'].iloc[-1]
            if 8 < pe_ratio < 15:
                pe_score = 15
            elif 15 <= pe_ratio < 25:
                pe_score = 10
            elif 25 <= pe_ratio < 35:
                pe_score = 5
            else:
                pe_score = 0

        if '市净率' in df.columns and pd.notna(df['市净率'].iloc[-1]):
            pb_ratio = df['市净率'].iloc[-1]
            if 0.8 < pb_ratio < 2:
                pb_score = 10
            elif 2 <= pb_ratio < 3:
                pb_score = 5
            else:
                pb_score = 0

        valuation_score = pe_score + pb_score
        score_details['估值评分'] = valuation_score
        total_score += valuation_score

        # 2. 成长性 (20分)
        momentum_20d = alpha_factors.get('momentum_20d', 0)
        momentum_60d = alpha_factors.get('momentum_60d', 0)

        if momentum_60d > 20:
            growth_score = 20
        elif momentum_60d > 10:
            growth_score = 15
        elif momentum_60d > 0:
            growth_score = 10
        elif momentum_60d > -10:
            growth_score = 5
        else:
            growth_score = 0

        score_details['成长性评分'] = growth_score
        total_score += growth_score

        # 3. 流动性 (15分)
        turnover_ratio = alpha_factors.get('turnover_ratio', 1)
        volume_ratio_20d = alpha_factors.get('volume_ratio_20d', 1)

        if turnover_ratio > 1.2 and volume_ratio_20d > 1.1:
            liquidity_score = 15
        elif turnover_ratio > 1.0 and volume_ratio_20d > 1.0:
            liquidity_score = 10
        elif turnover_ratio > 0.8:
            liquidity_score = 5
        else:
            liquidity_score = 0

        score_details['流动性评分'] = liquidity_score
        total_score += liquidity_score

        # 4. 技术面配合 (15分)
        macd_signal = alpha_factors.get('macd_signal', 0)
        rsi_position = alpha_factors.get('rsi_position', 0)

        tech_score = 0
        if macd_signal > 0:
            tech_score += 8
        if -0.3 < rsi_position < 0.3:
            tech_score += 7

        score_details['技术面评分'] = tech_score
        total_score += tech_score

        # 5. 市场位置 (10分)
        price_position = alpha_factors.get('price_position', 0.5)

        if 0.3 < price_position < 0.7:
            position_score = 10
        elif 0.2 < price_position < 0.8:
            position_score = 7
        else:
            position_score = 3

        score_details['位置评分'] = position_score
        total_score += position_score

        # 6. 稳定性 (10分)
        volatility_20d = alpha_factors.get('volatility_20d', 10)

        if volatility_20d < 3:
            stability_score = 10
        elif volatility_20d < 5:
            stability_score = 7
        elif volatility_20d < 7:
            stability_score = 5
        else:
            stability_score = 0

        score_details['稳定性评分'] = stability_score
        total_score += stability_score

        # 7. 量价协同 (5分)
        price_volume_corr = alpha_factors.get('price_volume_corr', 0)

        if price_volume_corr > 0.3:
            pv_score = 5
        elif price_volume_corr > 0:
            pv_score = 3
        else:
            pv_score = 0

        score_details['量价协同评分'] = pv_score
        total_score += pv_score

        score_details['总分'] = total_score
        score_details['市值'] = df['总市值(元)'].iloc[-1]
        score_details['换手率'] = alpha_factors.get('turnover_mean_20d', 0)
        score_details['趋势'] = momentum_20d
        score_details['波动率'] = volatility_20d

        # 过滤基本面得分为0的股票
        if total_score == 0:
            return None

        return score_details

    except Exception as e:
        print(f"计算 {stock_code} 增强基本面评分失败: {e}")
        return None

def _get_risk_level(volatility, liquidity):
    """获取风险等级"""
    if volatility < 3 and liquidity > 1.2:
        return "低风险"
    elif volatility < 5 and liquidity > 1.0:
        return "中风险"
    else:
        return "高风险"

def predict_enhanced_price_movement(stock_code, alpha_factors, tech_signals, fund_score):
    """增强版价格走势预测"""
    try:
        momentum_5d = alpha_factors.get('momentum_5d', 0)
        momentum_20d = alpha_factors.get('momentum_20d', 0)
        momentum_60d = alpha_factors.get('momentum_60d', 0)

        tech_score = tech_signals.get('技术信号得分', 0)
        fundamental_score = fund_score.get('总分', 0)

        volatility_20d = alpha_factors.get('volatility_20d', 5)
        vol_adjust = min(1.5, max(0.5, 5 / volatility_20d))

        liquidity_factor = alpha_factors.get('turnover_ratio', 1)
        liquidity_adjust = min(1.3, max(0.7, liquidity_factor))

        prediction_5d = (
            momentum_5d * 0.2 +
            momentum_20d * 0.3 +
            tech_score * 0.3 +
            fundamental_score * 0.2
        ) / 10 * vol_adjust * liquidity_adjust

        prediction_15d = (
            momentum_20d * 0.25 +
            momentum_60d * 0.25 +
            tech_score * 0.25 +
            fundamental_score * 0.25
        ) / 8 * vol_adjust * liquidity_adjust

        confidence_factors = [
            min(100, tech_score * 2),
            min(100, fundamental_score * 1.5),
            min(100, (100 - volatility_20d * 10)),
            min(100, liquidity_factor * 50)
        ]

        confidence = sum(confidence_factors) / len(confidence_factors)
        confidence = min(95, max(30, confidence))

        return {
            '5日预测涨幅': round(prediction_5d, 2),
            '15日预测涨幅': round(prediction_15d, 2),
            '置信度': round(confidence, 1),
            '风险等级': _get_risk_level(volatility_20d, liquidity_factor)
        }

    except Exception as e:
        print(f"预测 {stock_code} 增强价格走势失败: {e}")
        return None